Alternate Cluster Trends (Mixed Methods)
Janette Avelar
2024-08-01
# start by creating cluster subsets
#subset values
deeper <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Deeper learning") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
ed_justice <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Ed justice") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
individualized <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Individualized") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
postsecondary <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Postsecondary") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
access <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Increasing access & supports") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
student_driven <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "Student-driven learning") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)
none <- cluster %>%
filter(proposed_cluster_for_preliminary_24_analysis == "None" |
proposed_cluster_for_preliminary_24_analysis == "None?") %>%
select(label = tag) %>%
left_join(labels, by = "label") %>%
pull(var)### DATA FUNCTIONS ###
#set up repeating schools - 2+ years
repeaters_2 <- dat %>%
select(school_id, year) %>%
unique() %>%
mutate(rate = 1) %>%
pivot_wider(names_from = "year",
values_from = "rate") %>%
rowwise() %>%
mutate(participate = sum(`2019`, `2021`, `2022`, `2023`, `2024`, na.rm = TRUE)) %>%
filter(participate > 1) %>%
pull(school_id)
#set up repeating schools - 3+ years
repeaters_3 <- dat %>%
select(school_id, year) %>%
unique() %>%
mutate(rate = 1) %>%
pivot_wider(names_from = "year",
values_from = "rate") %>%
rowwise() %>%
mutate(participate = sum(`2019`, `2021`, `2022`, `2023`, `2024`, na.rm = TRUE)) %>%
filter(participate > 2) %>%
pull(school_id)
#set up repeating schools - all years
repeaters_all <- dat %>%
select(school_id, year) %>%
unique() %>%
mutate(rate = 1) %>%
pivot_wider(names_from = "year",
values_from = "rate") %>%
rowwise() %>%
mutate(participate = sum(`2019`, `2021`, `2022`, `2023`, `2024`, na.rm = TRUE)) %>%
filter(participate == 5) %>%
pull(school_id)
#set up totals
tots <- dat %>%
group_by(year) %>%
summarize(total = n_distinct(school_id))
tots_2 <- dat %>%
filter(school_id %in% repeaters_2) %>%
group_by(year) %>%
summarize(total = n_distinct(school_id))
tots_3 <- dat %>%
filter(school_id %in% repeaters_3) %>%
group_by(year) %>%
summarize(total = n_distinct(school_id))
tots_all <- dat %>%
filter(school_id %in% repeaters_all) %>%
group_by(year) %>%
summarize(total = n_distinct(school_id))
#set up 3+ year tags
rep_tags <- dat %>%
group_by(var) %>%
summarize(num_years = n_distinct(year)) %>%
ungroup() %>%
filter(num_years >= 3) %>%
select(var) %>%
unique() %>%
pull(var)
#prep data function across schools
prep_across <- function(data, cluster){
dat <- data %>%
filter(var %in% cluster) %>%
group_by(var, year) %>%
summarize(n = sum(usage)) %>%
ungroup() %>%
left_join(tots, by = "year") %>%
mutate(pct = n/total) %>%
left_join(labels, by = "var") %>%
group_by(var) %>%
mutate(x_coord = max(year),
y_coord = pct[which.max(year)])
return(dat)
}
#prep data function within schools
prep_within <- function(data, cluster){
dat <- data %>%
filter(var %in% cluster) %>%
filter(school_id %in% repeaters_2) %>%
arrange(school_id, year) %>%
group_by(school_id) %>%
mutate(wave = dense_rank(year)) %>%
ungroup() %>%
arrange(school_id, year) %>%
group_by(school_id, var) %>%
mutate(change = usage - lag(usage)) %>%
ungroup() %>%
mutate(adds = ifelse(change == 1, 1, NA),
drops = ifelse(change == -1, 1, NA)) %>%
group_by(var, year) %>%
summarize(`Overall rate` = sum(usage, na.rm = TRUE),
`Average change` = sum(change, na.rm = TRUE),
Adds = sum(adds, na.rm = TRUE),
Drops = sum(drops, na.rm = TRUE)) %>%
ungroup() %>%
pivot_longer(cols = c(`Overall rate`, `Average change`, Adds, Drops),
names_to = "type",
values_to = "n") %>%
left_join(labels, by = "var") %>%
mutate(type = factor(type, levels = c("Overall rate", "Average change", "Adds", "Drops"))) %>%
#3.27.24 modification = remove 2019 & only present adds/drops
filter(year > 2019, type == "Adds" | type == "Drops")
return(dat)
}
#prep data function overall pct
prep_overall <- function(data, cluster, group, total){
dat <- data %>%
filter(var %in% cluster) %>%
filter(school_id %in% group) %>%
group_by(var, year) %>%
summarize(n = sum(usage, na.rm = TRUE)) %>%
ungroup() %>%
left_join(total, by = "year") %>%
mutate(pct = n/total) %>%
left_join(labels, by = "var") %>%
group_by(var) %>%
mutate(x_coord = max(year),
y_coord = pct[which.max(year)])
return(dat)
}
prep_net <- function(data){
data <- data %>%
pivot_wider(names_from = type,
values_from = n) %>%
group_by(var) %>%
mutate(net = Adds - Drops) %>%
ungroup() %>%
left_join(tots, by = "year") %>%
group_by(year, var, label) %>%
summarize(norm_net = net/total) %>%
ungroup() %>%
group_by(var) %>%
mutate(cum_norm_net = mean(norm_net)) %>% #average net change over time
ungroup() %>%
mutate(type = case_when(
cum_norm_net == max(cum_norm_net) ~ "Largest net change over time",
cum_norm_net == min(cum_norm_net) ~ "Smallest net change over time",
TRUE ~ "Average net change"
),
type = factor(type, levels = c("Largest net change over time", "Smallest net change over time", "Average net change"))) #specify order for palette
}
prep_net_3 <- function(data){
data <- data %>%
filter(var %in% rep_tags) %>%
mutate(max_cum = max(cum_norm_net),
min_cum = min(cum_norm_net)) %>%
group_by(var) %>%
mutate(type = case_when(
max_cum == cum_norm_net ~ "Largest net change over time",
min_cum == cum_norm_net ~ "Smallest net change over time",
TRUE ~ "Average net change"),
type = factor(type, levels = c("Largest net change over time", "Smallest net change over time", "Average net change")))
}### PLOT FUNCTIONS ###
#theme function - could not load branding file
transcend_cols = c("#1A4C81","#59C3B4","#EF464B","#ADE0EE")
theme_transcend = theme_gdocs(base_size = 14, base_family = "Open Sans") +
theme(
plot.title = element_text(family = "Bebas Neue", color = "black"),
#plot.subtitle = element_text(family = "Open Sans", size = rel(0.8)),
plot.background = element_blank(),
axis.text = element_text(colour = "black"),
axis.title = element_text(colour = "black"),
panel.border = element_rect(colour = "#4D4D4F"),
strip.text = element_text(size = rel(0.8)),
plot.margin = margin(10, 24, 10, 10, "pt")
)
#plot function for across schools
cluster_plot_across <- function(data, x, y, labels) {
#palette <- c("#BC2582", "#FFA630", "#FFDE42", "#99C24D", "#218380", "#D3B7D7")
data <- data %>%
mutate(wrapped_label = str_wrap(get(labels), 40))
labels <- data %>%
select(label = wrapped_label, x_coord, y_coord) %>%
unique()
plot <- ggplot(data, aes_string(x = x, y = y, color = "label")) +
geom_point() +
geom_line() +
theme_bw() +
scale_x_continuous(limits = c(2019, 2027),
expand = c(0.01, 0.5),
breaks = unique(data$year)) + #expand 0.025, .3
scale_y_continuous(limits = c(0, 1),
expand = c(0, 0),
labels = scales::percent_format(accuracy = 1)) +
theme(legend.position = "none") +
geom_text_repel(data = labels,
aes(label = label, x = x_coord, y = y_coord),
nudge_x = 0.5,
direction = "y",
size = 3,
box.padding = 0.3,
point.padding = 2
) +
theme_transcend
return(plot)
}
cluster_plot_within <- function(data, facet) { #data, x, y, facet, group
plot <- ggplot(data, aes_string(x = "year", y = "n")) + #aes_string(x = x, y = y, text = group)
geom_line(aes(group = label), color = "gray", alpha = 0.5) + #group = group
geom_point(aes(group = label), color = "gray", alpha = 0.5, size = 1) + #group = group
geom_smooth(se = FALSE, method = "lm", color = "cornflowerblue", aes(group = 1)) +
#straight line before
#geom_smooth(method = "lm", se = FALSE, color = "blue", aes(group = 1)) +
theme_bw() +
scale_x_continuous(name = "Year", expand = expansion(mult = c(.025, .3))) +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none") +
facet_wrap(as.formula(paste0("~", facet))) + #, scales = "free_y"
#labs(x = x, y = y)
theme_transcend
return(plot)
}
net_change_plot <- function(data){
palette <- c("#6cac8c", "#ff6f69","#d3d3d3")
plot <- ggplot(data, aes(year, norm_net, color = type, group = var, text = label)) +
geom_line(alpha = 0.8) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = palette) +
theme_bw() +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none") +
scale_x_continuous(expand = expansion(mult = c(0, 0))) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(x = "", y = "Normalized net change",
subtitle = "Values above the dashed line indicate growth. Values below the dashed line indicate decline.") +
geom_hline(aes(yintercept = 0), size = .25, linetype = "dashed") +
theme_transcend
interactive <- ggplotly(plot, tooltip = c("year", "norm_net", "label"))
return(interactive)
}#try plotly
#across plot
fancy_across <- function(data){
plot <- plot_ly(data, x = ~year, y = ~pct, type = 'scatter', mode = 'lines+markers', text = ~label, hoverinfo = 'text+y') %>%
layout(yaxis = list(tickformat = ".0%"))
return(plot)
}Deeper Learning Cluster
Tags:
- assessments for deeper learning
- competency/mastery-based education
- competency framework
- design thinking process
- interdisciplinary
- makerspace
- multiple opportunities to demonstrate mastery
- project-based learning
- performance based assessment
- place-based learning
- student-led conferences
- students develop projects
Guiding question: How have the rates of deeper learning tags changed each year since 2019?
Across schools
deep <- prep_across(dat, deeper)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(deep)Within schools
Overall rate among repeat schools (2+ years):
deep <- prep_overall(dat, deeper, repeaters_2, tots_2)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(deep)Overall rate among repeat schools (3+ years):
deep <- prep_overall(dat, deeper, repeaters_3, tots_3)
fancy_across(deep)Overall rate among repeat schools (5 years):
deep <- prep_overall(dat, deeper, repeaters_all, tots_all)
fancy_across(deep)deep <- prep_within(dat, deeper)
plot <- cluster_plot_within(deep, "type") #deep, deep$year, deep$n, "type", deep$label## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.suppressWarnings({ggplotly(plot)})Net change
In the following graph I used net change (Adds - Drops) to calculate the rate of change for each tag. I then created a cumulative rate for each tag (average across years) to identify the highest and lowest rates of change in the cluster. The highest rate of change is colored in green and the lowest rate of change in red.
Note these plots are interactive–if you’re curious about a particular line you can hover over one of it’s points to see which tag it corresponds to.
deep <- prep_net(deep)
net_change_plot(deep)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the plotly package.
## Please report the issue at <https://github.com/plotly/plotly.R/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Educational Justice Cluster
Tags:
- adult wellness/SEL
- anti-racist practices
- assessments for social-emotional skills
- teachers as co-leaders
- co-leadership
- family and community support services
- culturally responsive practices
- design to meet needs of students who have been marginalized
- hiring for equity and inclusion values
- all courses designed for inclusion
- mental health services
- physical well being services
- reallocation of resources for those most in need
- restorative practices
- SEL curriculum
- SEL integration school-wide
- social justice focus
- translanguaging
- trauma-informed practices
Across schools
justice_dat <- prep_across(dat, ed_justice)
fancy_across(justice_dat)Within schools
Overall rate among repeat schools (2+ years):
justice_dat <- prep_overall(dat, ed_justice, repeaters_2, tots_2)
fancy_across(justice_dat)Overall rate among repeat schools (3+ years):
justice_dat <- prep_overall(dat, ed_justice, repeaters_3, tots_3)
fancy_across(justice_dat)Overall rate among repeat schools (all years):
justice_dat <- prep_overall(dat, ed_justice, repeaters_all, tots_all)
fancy_across(justice_dat)justice_dat <- prep_within(dat, ed_justice)
plot <- cluster_plot_within(justice_dat, "type")
suppressWarnings({ggplotly(plot)})Net change
justice_dat <- prep_net(justice_dat)
net_change_plot(justice_dat)Individualized Cluster
Tags:
- accommodations provided to all students
- blended learning
- flex model
- flipped classroom
- interoperable data from multiple technologies
- 1:1 mentoring
- station rotation
- students access their own data
- self-paced learning
Across schools
ind_dat <- prep_across(dat, individualized)
fancy_across(ind_dat)Within schools
Overall rate among repeat schools (2+ years):
ind_dat <- prep_overall(dat, individualized, repeaters_2, tots_2)
fancy_across(ind_dat)Overall rate among repeat schools (3+ years):
ind_dat <- prep_overall(dat, individualized, repeaters_3, tots_3)
fancy_across(ind_dat)Overall rate among repeat schools (all years):
ind_dat <- prep_overall(dat, individualized, repeaters_3, tots_3)
fancy_across(ind_dat)ind_dat <- prep_within(dat, individualized)
plot <- cluster_plot_within(ind_dat, "type")
suppressWarnings({ggplotly(plot)})Net change
ind_dat <- prep_net(ind_dat)
net_change_plot(ind_dat)Postsecondary Cluster
Tags:
- Ã la carte model
- assessments for career readiness
- career prep and work-based learning
- community-based organizations as co-leaders
- industry-based partners as co-leaders
- community and business partnerships
- early college high school
- extended learning opportunities
- students earn industry credentials
Across schools
post_dat <- prep_across(dat, postsecondary)
fancy_across(post_dat)Within schools
Overall rate among repeat schools (2+ years):
post_dat <- prep_overall(dat, postsecondary, repeaters_2, tots_2)
fancy_across(post_dat)Overall rate among repeat schools (3+ years):
post_dat <- prep_overall(dat, postsecondary, repeaters_3, tots_3)
fancy_across(post_dat)Overall rate among repeat schools (all years):
post_dat <- prep_overall(dat, postsecondary, repeaters_all, tots_all)
fancy_across(post_dat)post_dat <- prep_within(dat, postsecondary)
plot <- cluster_plot_within(post_dat, "type")
suppressWarnings({ggplotly(plot)})Net change
post_dat <- prep_net(post_dat)
net_change_plot(post_dat)High Schools only
# set up data prep
# ID high schools
hs <- import(here("data/longitudinal", "longitudinal_data.csv")) %>%
select(school_id, grades_high) %>%
filter(grades_high == 1) %>%
pull(school_id)
#set up repeating schools
hs_repeaters <- dat %>%
filter(school_id %in% hs) %>%
select(school_id, year) %>%
unique() %>%
mutate(rate = 1) %>%
pivot_wider(names_from = "year",
values_from = "rate") %>%
rowwise() %>%
mutate(participate = sum(`2019`, `2021`, `2022`, `2023`, `2024`, na.rm = TRUE)) %>%
filter(participate > 1) %>%
pull(school_id)
#set up totals
hs_tots <- dat %>%
filter(school_id %in% hs) %>%
group_by(year) %>%
summarize(total = n_distinct(school_id)) %>%
unique()
#prep data across schools
hs_across <- dat %>%
filter(school_id %in% hs) %>%
filter(var %in% postsecondary) %>%
group_by(var, year) %>%
summarize(n = sum(usage)) %>%
ungroup() %>%
left_join(hs_tots, by = "year", relationship = "many-to-many") %>%
mutate(pct = n/total) %>%
left_join(labels, by = "var") %>%
group_by(var) %>%
mutate(x_coord = max(year),
y_coord = pct[which.max(year)])
#prep data within schools
hs_within <- dat %>%
filter(school_id %in% hs) %>%
filter(var %in% postsecondary) %>%
filter(school_id %in% hs_repeaters) %>%
arrange(school_id, year) %>%
group_by(school_id) %>%
mutate(wave = dense_rank(year)) %>%
ungroup() %>%
arrange(school_id, year) %>%
group_by(school_id, var) %>%
mutate(change = usage - lag(usage)) %>%
ungroup() %>%
mutate(adds = ifelse(change == 1, 1, NA),
drops = ifelse(change == -1, 1, NA)) %>%
group_by(var, year) %>%
summarize(`Overall rate` = sum(usage, na.rm = TRUE),
`Average change` = sum(change, na.rm = TRUE),
Adds = sum(adds, na.rm = TRUE),
Drops = sum(drops, na.rm = TRUE)) %>%
ungroup() %>%
pivot_longer(cols = c(`Overall rate`, `Average change`, Adds, Drops),
names_to = "type",
values_to = "n") %>%
left_join(labels, by = "var") %>%
mutate(type = factor(type, levels = c("Overall rate", "Average change", "Adds", "Drops"))) %>%
#3.27.24 modification = remove 2019 & only present adds/drops
filter(year > 2019, type == "Adds" | type == "Drops")
#prep data overall pct
hs_overall <- dat %>%
filter(var %in% postsecondary) %>%
filter(school_id %in% hs_repeaters) %>%
group_by(var, year) %>%
summarize(n = sum(usage, na.rm = TRUE)) %>%
ungroup() %>%
left_join(hs_tots, by = "year") %>%
mutate(pct = n/total) %>%
left_join(labels, by = "var") %>%
group_by(var) %>%
mutate(x_coord = max(year),
y_coord = pct[which.max(year)])Across high schools
fancy_across(hs_across)Within high schools
Overall rate among repeat high schools (2+ years):
fancy_across(hs_overall)plot <- cluster_plot_within(hs_within, "type")
suppressWarnings({ggplotly(plot)})Net change
hs_within <- prep_net(hs_within)
net_change_plot(hs_within)Student-driven Learning Cluster
Tags:
- assessments for agency & self-directed learning
- families as co-leaders
- grading policies focus on mastery
- peer to peer support
- student advisories
- students as co-leaders
- student-led goal setting
Guiding question: How have the rates of student-driven learning tags changed each year since 2019?
Across schools
drive <- prep_across(dat, student_driven)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(drive)Within schools
Overall rate among repeat schools (2+ years):
drive <- prep_overall(dat, student_driven, repeaters_2, tots_2)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(drive)Overall rate among repeat schools (3+ years):
drive <- prep_overall(dat, student_driven, repeaters_3, tots_3)
fancy_across(drive)Overall rate among repeat schools (5 years):
drive <- prep_overall(dat, student_driven, repeaters_all, tots_all)
fancy_across(drive)drive <- prep_within(dat, student_driven)
plot <- cluster_plot_within(drive, "type") #deep, deep$year, deep$n, "type", deep$label
suppressWarnings({ggplotly(plot)})Net change
In the following graph I used net change (Adds - Drops) to calculate the rate of change for each tag. I then created a cumulative rate for each tag (average across years) to identify the highest and lowest rates of change in the cluster. The highest rate of change is colored in green and the lowest rate of change in red.
Note these plots are interactive–if you’re curious about a particular line you can hover over one of it’s points to see which tag it corresponds to.
drive <- prep_net(drive)
net_change_plot(drive)Increasing Access & Supports Cluster
Tags:
- expanded open hours
- flexible facilities & classroom design
- flexible staffing & alternative teaching roles
- multi-tiered sysems of support (MTSS)
- no tracked classes
- tutoring
- universal design for learning
Guiding question: How have the rates of increasing access & supports tags changed each year since 2019?
Across schools
access_supports <- prep_across(dat, access)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(access_supports)Within schools
Overall rate among repeat schools (2+ years):
access_supports <- prep_overall(dat, access, repeaters_2, tots_2)
#cluster_plot_across(deep, "year", "pct", "label")
fancy_across(access_supports)Overall rate among repeat schools (3+ years):
access_supports <- prep_overall(dat, access, repeaters_3, tots_3)
fancy_across(access_supports)Overall rate among repeat schools (5 years):
access_supports <- prep_overall(dat, access, repeaters_all, tots_all)
fancy_across(access_supports)access_supports <- prep_within(dat, access)
plot <- cluster_plot_within(access_supports, "type") #deep, deep$year, deep$n, "type", deep$label
suppressWarnings({ggplotly(plot)})Net change
In the following graph I used net change (Adds - Drops) to calculate the rate of change for each tag. I then created a cumulative rate for each tag (average across years) to identify the highest and lowest rates of change in the cluster. The highest rate of change is colored in green and the lowest rate of change in red.
Note these plots are interactive–if you’re curious about a particular line you can hover over one of it’s points to see which tag it corresponds to.
access_supports <- prep_net(access_supports)
net_change_plot(access_supports)Blended Learning Cluster
Reminder: This cluster is separate from the analysis - this was a starting point.
Tags: - blended learning
- Ã la carte model
- flipped classroom
- flex model
- enriched virtual model
- station rotation
Across schools
# subset tags
blended_tags <- c("practices_blended_learning", "practices_a_la_carte", "practices_flipped_classroom", "practices_flex", "practices_enriched_virtual", "practices_station_rotation")
blend_dat <- prep_across(dat, blended_tags)
fancy_across(blend_dat)Within schools
Overall rate among repeat schools (2+ years):
blend_dat <- prep_overall(dat, blended_tags, repeaters_2, tots_2)
fancy_across(blend_dat)Overall rate among repeat schools (3+ years):
blend_dat <- prep_overall(dat, blended_tags, repeaters_3, tots_3)
fancy_across(blend_dat)Overall rate among repeat schools (all years):
blend_dat <- prep_overall(dat, blended_tags, repeaters_all, tots_all)
fancy_across(blend_dat)blend_dat <- prep_within(dat, blended_tags)
plot <- cluster_plot_within(blend_dat, "type")
suppressWarnings({ggplotly(plot)})Net change
blend_dat <- prep_net(blend_dat)
net_change_plot(blend_dat)All Clusters
Overall rate change among repeat schools (2+ years)
merge_deep <- prep_overall(dat, deeper, repeaters_2, tots_2) %>%
mutate(cluster = "Deeper learning")
merge_ed_justice <- prep_overall(dat, ed_justice, repeaters_2, tots_2) %>%
mutate(cluster = "Educational justice")
merge_individualized <- prep_overall(dat, individualized, repeaters_2, tots_2) %>%
mutate(cluster = ("Individualized learning"))
merge_postsecondary <- prep_overall(dat, postsecondary, repeaters_2, tots_2) %>%
mutate(cluster = "Postsecondary pathways")
merge_access <- prep_overall(dat, access, repeaters_2, tots_2) %>%
mutate(cluster = "Increasing access & supports")
merge_drive <- prep_overall(dat, student_driven, repeaters_2, tots_2) %>%
mutate(cluster = "Student-driven learning")
merge_none <- prep_overall(dat, none, repeaters_2, tots_2) %>%
mutate(cluster = "No cluster")
merge_blended <- prep_overall(dat, blended_tags, repeaters_2, tots_2) %>%
mutate(cluster = "Blended learning (alternate)")
all_clust <- bind_rows(merge_deep, merge_ed_justice, merge_individualized, merge_postsecondary, merge_access, merge_drive, merge_none, merge_blended)
all_clust %>%
ggplot(., aes(year, pct)) +
#geom_line(aes(group = label), color = "gray", alpha = 0.5) +
#geom_point(aes(group = label), color = "gray", alpha = 0.5, size = 1) +
geom_smooth(se = FALSE, method = "loess", color = "cornflowerblue", aes(group = 1)) +
theme_bw() +
scale_x_continuous(breaks = unique(all_clust$year)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1),
limits = c(0, 1)) +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none") +
facet_wrap(~cluster) +
labs(x = "", y = "Rate of selection")## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 2.7613e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 9.1583e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.2422e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 5.4838e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 9.4047e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2024## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2.025## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 8.3411e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.3358e-16## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 2021## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.5491e-16
rm(merge_deep, merge_ed_justice, merge_individualized, merge_postsecondary, merge_none, merge_blended, merge_access, merge_drive)Overall rate change among repeat schools (2+ years) using stable tags (3+ years)
all_clust %>%
filter(var %in% rep_tags) %>%
filter(cluster != "Blended learning (alternate)", cluster != "No cluster") %>%
ggplot(., aes(year, pct, color = cluster)) +
#geom_line(aes(group = label), color = "gray", alpha = 0.5) +
#geom_point(aes(group = label), color = "gray", alpha = 0.5, size = 1) +
geom_smooth(se = FALSE, method = "lm", aes(group = cluster)) + #color = "cornflowerblue", aes(group = 1)
#scale_color_manual(values = transcend_cols) +
theme_bw() +
scale_x_continuous(breaks = unique(all_clust$year),
expand = c(0,0)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1),
limits = c(0, 1),
expand = c(0,0)) +
#facet_wrap(~cluster) +
labs(x = "", y = "Rate of selection", color = "Cluster") +
theme_transcend +
theme(legend.position = "bottom", legend.direction = "horizontal") + #panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(),
guides(color = guide_legend(ncol = 2))
Overall growth rate among repeat schools (2+ years) using stable tags (3+ years)
merge_deep <- deep %>%
mutate(cluster = "Deeper learning")
merge_ed_justice <- justice_dat %>%
mutate(cluster = "Educational justice")
merge_individualized <- ind_dat %>%
mutate(cluster = ("Individualized learning"))
merge_postsecondary <- post_dat %>%
mutate(cluster = "Postsecondary pathways")
merge_access <- access_supports %>%
mutate(cluster = "Increasing access & supports")
merge_driven <- drive %>%
mutate(cluster = "Student-driven learning")
merge_none <- none_dat %>%
mutate(cluster = "No cluster")
merge_blended <- blend_dat %>%
mutate(cluster = "Blended learning (alternate)")
all_clust <- bind_rows(merge_deep, merge_ed_justice, merge_individualized, merge_postsecondary, merge_access, merge_driven, merge_none, merge_blended) %>%
filter(var %in% rep_tags)
all_clust %>%
ggplot(., aes(year, norm_net)) +
geom_line(aes(group = label), color = "gray", alpha = 0.5) +
geom_point(aes(group = label), color = "gray", alpha = 0.5, size = 1) +
geom_smooth(se = FALSE, method = "lm", color = "cornflowerblue", aes(group = 1)) +
geom_hline(aes(yintercept = 0), size = .25, linetype = "dashed") +
theme_bw() +
scale_x_continuous(breaks = unique(all_clust$year)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none") +
facet_wrap(~cluster) +
labs(x = "", y = "Growth rate")
Net change
merge_deep <- deep %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Deeper learning") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_ed_justice <- justice_dat %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Educational Justice") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_individualized <- ind_dat %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Individualized learning") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_postsecondary <- post_dat %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Postsecondary pathways") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_access <- access_supports %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Increasing access & supports") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_drive <- drive %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Student-driven learning") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_none <- none_dat %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "No cluster") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
merge_blended <- blend_dat %>%
select(year, var, label, norm_net) %>%
mutate(cluster = "Blended learning (alternate)") %>%
group_by(year, cluster) %>%
summarize(cum_norm_net = mean(norm_net)) %>%
ungroup()
all_clust <- bind_rows(merge_deep, merge_ed_justice, merge_individualized, merge_postsecondary, merge_access, merge_drive, merge_none, merge_blended) %>%
rename("norm_net" = cum_norm_net) %>%
group_by(cluster) %>%
mutate(cum_norm_net = mean(norm_net)) %>%
ungroup() %>%
mutate(type = case_when(
cum_norm_net == max(cum_norm_net) ~ "Largest net change over time",
cum_norm_net == min(cum_norm_net) ~ "Smallest net change over time",
TRUE ~ "Average net change"
),
type = factor(type, levels = c("Largest net change over time", "Smallest net change over time", "Average net change")),
label = cluster) %>%
rename("var" = cluster)
net_change_plot(all_clust)rm(merge_blended, merge_deep, merge_individualized, merge_postsecondary, merge_ed_justice, merge_none)Net change (excluding alternate blended learning cluster)
all_clust <- all_clust %>%
filter(var != "Blended learning (alternate)") %>%
mutate(max_cum = max(cum_norm_net),
min_cum = min(cum_norm_net)) %>%
group_by(var) %>%
mutate(type = case_when(
max_cum == cum_norm_net ~ "Largest net change over time",
min_cum == cum_norm_net ~ "Smallest net change over time",
TRUE ~ "Average net change"),
type = factor(type, levels = c("Largest net change over time", "Smallest net change over time", "Average net change")))
net_change_plot(all_clust)Net change (static version with labels)
static <- all_clust %>%
filter(var != "Blended learning (alternate)", var != "No cluster") %>%
ungroup() %>%
mutate(max_cum = max(cum_norm_net),
min_cum = min(cum_norm_net),
label = case_when(
year != 2024 ~ NA,
TRUE ~ as.character(label)
)) %>%
group_by(var) %>%
mutate(type = case_when(
max_cum == cum_norm_net ~ "Largest net change over time",
min_cum == cum_norm_net ~ "Smallest net change over time",
TRUE ~ "Average net change"),
type = factor(type, levels = c("Largest net change over time", "Smallest net change over time", "Average net change")),
x_coord = 2024.5,
y_coord = norm_net[which.max(year)]) %>%
ungroup()
ggplot(static, aes(year, norm_net, color = var, group = var, text = label)) +
geom_line() +
geom_point(alpha = 0.5, size = 1) +
#scale_color_manual(values = transcend_cols) +
theme_transcend +
theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), legend.position = "none") +
scale_x_continuous(expand = expansion(mult = c(0, .25))) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(x = "", y = "Normalized net change",
subtitle = "Values above the dashed line indicate growth. Values below the dashed line indicate decline.") +
geom_hline(aes(yintercept = 0), size = .25, linetype = "dashed") +
geom_text_repel(data = static,
aes(label = label, x = 2024, y = y_coord),
nudge_x = 0.05,
direction = "y",
hjust = 0,
size = 4,
point.padding = 1)## Warning: Removed 18 rows containing missing values (`geom_text_repel()`).
Models
#clean up environment
rm(cluster, deep, justice_dat, tots, tots_2, tots_3, tots_all)
#create new cluster DV
mod_dat <- dat %>%
#add cluster groups
mutate(cluster = case_when(
var %in% deeper ~ "deeper",
var %in% ed_justice ~ "ed_justice",
var %in% individualized ~ "individualized",
var %in% postsecondary ~ "postsecondary",
var %in% access ~ "access_supports",
var %in% drive ~ "student_driven",
var %in% none ~ "misc"
),
year = factor(year)) %>%
#note: clusters only outlined for 2024 tags... do we want to modify?
#for now, drop missing cluster
filter(!is.na(cluster)) %>%
#generate cluster percentage for each school
group_by(cluster, year, school_id) %>%
summarize(total = n_distinct(var),
n = sum(usage),
pct = n/total) %>%
select(school_id, year, cluster, n, pct) %>%
ungroup() %>%
pivot_wider(names_from = cluster,
values_from = c(n, pct))
#read in characteristic vars for modeling and merge
vars <- import(here("data/longitudinal", "longitudinal_data.csv")) %>%
select(school_id, year, school_type, school_locale, school_enrollment, pct_bipoc, pct_ell, pct_frpl, pct_swd, grades_pk, grades_elementary, grades_middle, grades_high) %>%
mutate(c_enrollment = scale(school_enrollment, center = TRUE, scale = TRUE)[,1],
c_bipoc = scale(pct_bipoc, center = TRUE, scale = TRUE)[,1],
c_ell = scale(pct_ell, center = TRUE, scale = TRUE)[,1],
c_frpl = scale(pct_frpl, center = TRUE, scale = TRUE)[,1],
c_swd = scale(pct_swd, center = TRUE, scale = TRUE)[,1],
year = factor(year)) %>%
select(-c(school_enrollment, starts_with("pct")))
#merge
mod_dat <- mod_dat %>%
left_join(vars, by = c("school_id", "year"))
# set vars of interest
dv <- mod_dat %>% select(starts_with("n_")) %>% colnames()
preds <- mod_dat %>% select(-school_id, -starts_with("pct_"), -starts_with("n_")) %>% colnames()Poisson
#run models
model_list <- list()
for (outcome in dv) {
formula <- as.formula(paste(dv, "~", paste(preds, collapse = "+")))
model <- glm(formula, data = mod_dat, family = "poisson")
model_list[[outcome]] <- model
}## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.
## Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
## Consider formula(paste(x, collapse = " ")) instead.#extract coeffs
tidy_models <- list()
for (outcome in names(model_list)) {
tidy_model <- tidy(model_list[[outcome]])
tidy_model$outcome <- outcome
tidy_models[[outcome]] <- tidy_model
}
#combine
plot_data <- do.call(rbind, tidy_models) %>%
#rename outcome
mutate(outcome = case_when(
outcome == "n_deeper" ~ "Deeper learning",
outcome == "n_ed_justice" ~ "Educational justice",
outcome == "n_individualized" ~ "Individualized learning",
outcome == "n_postsecondary" ~ "Postsecondary pathways",
outcome == "n_misc" ~ "Misc. cluster",
outcome == "n_access_supports" ~ "Increasing access & supports",
outcome == "n_student_driven" ~ "Student-driven learning"
),
#rename terms
term = str_remove_all(term, "school_type"),
term = str_remove_all(term, "grades_"),
term = str_remove_all(term, "c_"),
term = str_remove_all(term, "school_locale")
) %>%
arrange(desc(estimate)) %>%
mutate(term = fct_inorder(term))#prep data
plot_data_list <- list()
for (outcome in names(model_list)) {
tidy_model <- tidy(model_list[[outcome]], conf.int = TRUE)
tidy_model$outcome <- outcome
plot_data_list[[outcome]] <- tidy_model
}
#create plot for each outcome
create_plot_for_outcome <- function(data, outcome) {
data <- data %>%
filter(term != "(Intercept)")
ggplot(data, aes(y = fct_reorder(term, estimate), x = estimate)) +
geom_linerange(aes(xmin = estimate - std.error,
xmax = estimate + std.error),
color = "blue") +
geom_point() +
theme_minimal() +
theme(panel.grid.major.y = element_blank()) +
labs(
x = "Estimated effect on cluster",
y = "",
title = sprintf("School characteristics describing %s", outcome)
)
}
plots <- list()
for (outcome in names(plot_data_list)) {
plots[[outcome]] <- create_plot_for_outcome(plot_data_list[[outcome]], outcome)
}
plots$n_deeper
plots$n_ed_justice
plots$n_individualized
plots$n_misc
plots$n_postsecondary
plots$n_access_supports
plots$n_student_driven## NULL